Pipeline Utilities

Node

A Node is a "unit of computation" in the execution of phenotypes and cohorts in PhenEx. Each Node outputs a single table and the output table is the smallest unit of computation in PhenEx that can be materialized to a database. Anything you want to "checkpoint-able" should be encapsulated within a Node. Smaller units of calculation are never materialized.

A Node is defined by an arbitrary set of user-specified parameters and can have arbitrary dependencies. Abstractly, a Node represents a node in the directed acyclic graph (DAG) that determines the order of execution of dependencies for a given Phenotype (which is itself a Node). The Node class manages the execution of itself and any dependent nodes, optionally using lazy (re)execution for making incremental updates to a Node object.

The user injects the logic for producing the output table from the input parameters and dependent nodes by subclassing Node.

To subclass

1. Define the parameters required to compute the Node in the __init__() interface.
2. At the top of __init__(), call super().init(). This must be called before any calls to add_children().
3. Register children nodes - Node's which must be executed before the current Node - by calling add_children(), allowing Node's to be executed recursively. You only need to add direct dependencies as children. Deeper dependencies (children of children) are automatically inferred.
4. Define self._execute(). The self._execute() method is reponsible for interpreting the input parameters to the Node and returning the appropriate Table.
5. Define tests in phenex.test! High test coverage gives us confidence that our answers are correct and makes it easier to make changes to the code later on.

Parameters:

Name	Type	Description	Default
name	Optional[str]	A short, descriptive, and unique name for the node. The name is used as a unique identifier for the node and cannot be the same as the name of any dependent node (you cannot have two dependent nodes called "codelist_phenotype", for example). If the output table is materialized from this node, name will be used as the table name in the database.	None

Attributes:

Name	Type	Description
table		The stored output from call to self.execute().

Example

class MyNode(Node):
    def __init__(self, name, other_node, threshold=10):

        # call super() at the TOP of __init__()
        super(MyNode, self).__init__(name=name)
        self.other_node = other_node
        self.threshold = threshold

        # Add any dependent nodes
        self.add_children(other_node)

    def _execute(self, tables):
        # Your computation logic here
        return some_table

Source code in phenex/node.py

class Node:
    """
    A Node is a "unit of computation" in the execution of phenotypes and cohorts in PhenEx. Each Node outputs a single table and the output table is the smallest unit of computation in PhenEx that can be materialized to a database. Anything you want to "checkpoint-able" should be encapsulated within a Node. Smaller units of calculation are never materialized.

    A Node is defined by an arbitrary set of user-specified parameters and can have arbitrary dependencies. Abstractly, a Node represents a node in the directed acyclic graph (DAG) that determines the order of execution of dependencies for a given Phenotype (which is itself a Node). The Node class manages the execution of itself and any dependent nodes, optionally using lazy (re)execution for making incremental updates to a Node object.

    The user injects the logic for producing the output table from the input parameters and dependent nodes by subclassing Node.

    To subclass:
        1. Define the parameters required to compute the Node in the `__init__()` interface.
        2. At the top of `__init__()`, call super().__init__(). This must be called before any calls to `add_children()`.
        3. Register children nodes - Node's which must be executed before the current Node - by calling `add_children()`, allowing Node's to be executed recursively. You only need to add direct dependencies as children. Deeper dependencies (children of children) are automatically inferred.
        4. Define `self._execute()`. The `self._execute()` method is reponsible for interpreting the input parameters to the Node and returning the appropriate Table.
        5. Define tests in `phenex.test`! High test coverage gives us confidence that our answers are correct and makes it easier to make changes to the code later on.

    Parameters:
        name: A short, descriptive, and unique name for the node. The name is used as a unique identifier for the node and cannot be the same as the name of any dependent node (you cannot have two dependent nodes called "codelist_phenotype", for example). If the output table is materialized from this node, name will be used as the table name in the database.

    Attributes:
        table: The stored output from call to self.execute().

    Example:
        ```python
        class MyNode(Node):
            def __init__(self, name, other_node, threshold=10):

                # call super() at the TOP of __init__()
                super(MyNode, self).__init__(name=name)
                self.other_node = other_node
                self.threshold = threshold

                # Add any dependent nodes
                self.add_children(other_node)

            def _execute(self, tables):
                # Your computation logic here
                return some_table
        ```
    """

    def __init__(self, name: Optional[str] = None):
        self._name = name or type(self).__name__
        self._children = []
        self.table = None  # populated upon call to execute()

    def add_children(self, children):
        if not isinstance(children, list):
            children = [children]
        for child in children:
            if self._check_child_can_be_added(child):
                self._children.append(child)

    def __rshift__(self, right):
        self.add_children(right)
        return right

    def _check_child_can_be_added(self, child):
        """
        Checks that child node can be added to self.children. A child node must:
            1. Be of type Node
            2. Not already be in self.children
            3. Does not create a circular dependency and
            4. Have a unique name from all other dependencies
        """
        if not isinstance(child, Node):
            raise ValueError("Dependent children must be of type Node!")

        if any([c is child for c in self.children]):
            # note to use IS and not IN since IN check __eq__ and you can have
            # __eq__ nodes that are not the same. A bit pedantic of a point;
            # anyway, the check on duplicate names will raise an error but it's
            # useful for the user to know the difference, i.e., literally THIS
            # object has already been added or an otherwise identical object has
            # already been added
            raise ValueError(
                f"Duplicate node found: the node '{child.name}' has already been added to the list of children."
            )

        # Check for circular dependencies: ensure that self is not already a dependency of child
        if self in child.dependencies:
            raise ValueError(
                f"Circular dependency detected: adding '{child.name}' as a child of '{self.name}' "
                f"would create a circular dependency because '{self.name}' is already a dependency of '{child.name}'."
            )

        for dep in self.dependencies:
            if child.name == dep.name and child is not dep:
                raise ValueError(
                    f"Duplicate node name found: the name '{child.name}' is used both for this node and one of its dependencies."
                )

        return True

    @property
    def children(self):
        # implementation of children as a property to prevent direct modification
        return self._children[:]

    @property
    def dependencies(self) -> Set["Node"]:
        """
        Recursively collect all dependencies of a node (including dependencies of dependencies).

        Returns:
            List[Node]: A list of Node objects on which this Node depends.
        """
        # always recompute the dependencies because you don't know if a child has invalidated the dependency list
        return list(self._collect_all_dependencies().values())

    @property
    def dependency_graph(self) -> Dict["Node", Set["Node"]]:
        """
        Build a dependency graph where each node maps to its direct dependencies (children).

        Returns:
            Dict[Node, Set[Node]: A mapping of Node's to their children Node's.
        """
        graph = defaultdict(set)
        graph[self] = self.children

        for node in self.dependencies:
            graph[node] = node.children
        return dict(graph)

    @property
    def reverse_dependency_graph(self) -> Dict["Node", Set["Node"]]:
        """
        Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

        Returns:
            Dict[Node, List[Node]: A mapping of Node's to their parent Node's.
        """
        reverse_graph = defaultdict(set)
        for parent, children in self.dependency_graph.items():
            for child in children:
                reverse_graph[child].add(parent)
        return dict(reverse_graph)

    def _collect_all_dependencies(self, visited: Set[str] = None) -> Dict[str, "Node"]:
        if visited is None:
            visited = set()

        all_deps = {}

        # Avoid infinite loops with circular dependencies
        if self.name in visited:
            return all_deps
        visited.add(self.name)

        # Add direct children (dependencies)
        for child in self.children:
            if child.name not in all_deps:
                all_deps[child.name] = child
                # Recursively collect dependencies of this child
                child_deps = child._collect_all_dependencies(visited.copy())
                all_deps.update(child_deps)

        return all_deps

    @property
    def name(self):
        return self._name.upper()

    @name.setter
    def name(self, name):
        self._name = name

    def _get_last_hash(self):
        """
        Retrieve the hash of the node's defining parameters from the last time it was computed. This hash is stored in a local DuckDB database.

        Returns:
            str: The MD5 hash of the node's attributes as a hexadecimal string.
        """
        con = DuckDBConnector(DUCKDB_DEST_DATABASE=NODE_STATES_DB_NAME)
        if NODE_STATES_TABLE_NAME in con.dest_connection.list_tables():
            table = con.get_dest_table(NODE_STATES_TABLE_NAME).to_pandas()
            table = table[table.NODE_NAME == self.name]
            if len(table):
                return table[table.NODE_NAME == self.name].iloc[0].LAST_HASH

    def _get_current_hash(self):
        """
        Computes the hash of the node's defining parameters for change detection in lazy execution.

        Returns:
            str: The MD5 hash of the node's attributes as a hexadecimal string.
        """
        as_dict = self.to_dict()
        # to make sure that difference classes that take the same parameters return different hashes!
        as_dict["class"] = self.__class__.__name__
        dhash = hashlib.md5()
        # Use json.dumps to get a string, enforce sorted keys for deterministic ordering
        encoded = json.dumps(as_dict, sort_keys=True).encode()
        dhash.update(encoded)
        return int(dhash.hexdigest()[:8], 16)

    def __hash__(self):
        # For python built-in function hash().
        # Convert hex string to integer for consistent hashing
        return self._get_current_hash()

    def _update_current_hash(self):

        con = DuckDBConnector(DUCKDB_DEST_DATABASE=NODE_STATES_DB_NAME)

        df = pd.DataFrame.from_dict(
            {
                "NODE_NAME": [self.name],
                "LAST_HASH": [self._get_current_hash()],
                "NODE_PARAMS": [json.dumps(self.to_dict())],
            }
        )

        if NODE_STATES_TABLE_NAME in con.dest_connection.list_tables():
            table = con.get_dest_table(NODE_STATES_TABLE_NAME).to_pandas()
            table = table[table.NODE_NAME != self.name]
            df = pd.concat([table, df])

        table = ibis.memtable(df)
        con.create_table(table, name_table=NODE_STATES_TABLE_NAME, overwrite=True)

        return True

    def execute(
        self,
        tables: Dict[str, Table] = None,
        con: Optional[object] = None,
        overwrite: bool = False,
        lazy_execution: bool = False,
        n_threads: int = 1,
    ) -> Table:
        """
        Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

        Parameters:
            tables: A dictionary mapping domains to Table objects.
            con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.
            overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.
            lazy_execution: If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.
            n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

        Returns:
            Table: The resulting table for this node. Also accessible through self.table after calling self.execute().
        """
        # Handle None tables
        if tables is None:
            tables = {}

        # Use multithreaded execution if we have multiple children and n_threads > 1
        if len(self.children) > 1 and n_threads > 1:
            return self._execute_multithreaded(
                tables, con, overwrite, lazy_execution, n_threads
            )
        else:
            return self._execute_sequential(tables, con, overwrite, lazy_execution)

    def _execute_sequential(
        self,
        tables: Dict[str, Table] = None,
        con: Optional[object] = None,
        overwrite: bool = False,
        lazy_execution: bool = False,
    ) -> Table:
        """
        Execute the node and its dependencies sequentially.
        """
        if tables is None:
            tables = {}

        # First recursively execute all children nodes
        logger.info(f"Node '{self.name}': executing ...")
        for child in self.children:
            logger.info(f"Node '{self.name}': executing child node {child.name} ...")
            child.execute(
                tables=tables,
                con=con,
                overwrite=overwrite,
                lazy_execution=lazy_execution,
                n_threads=1,  # Sequential execution for children
            )

        # Execute current node
        if lazy_execution:
            if not overwrite:
                raise ValueError("lazy_execution only works with overwrite=True.")
            if con is None:
                raise ValueError(
                    "A DatabaseConnector is required for lazy execution. Computed tables will be materialized and only recomputed as needed."
                )

            # first time computing, _get_last_hash() will be None and execution will still be triggered
            if self._get_current_hash() != self._get_last_hash():
                logger.info(
                    f"Node '{self.name}': not yet computed or changed since last computation -- recomputing ..."
                )
                self.table = self._execute(tables)
                logger.info(f"Node '{self.name}': writing table to {self.name} ...")
                con.create_table(
                    self.table,
                    self.name,
                    overwrite=overwrite,
                )
                self._update_current_hash()
            else:
                logger.info(
                    f"Node '{self.name}': unchanged since last computation -- skipping!"
                )
                self.table = con.get_dest_table(self.name)

        else:
            self.table = self._execute(tables)
            if con:
                logger.info(f"Node '{self.name}': writing table to {self.name} ...")
                con.create_table(
                    self.table,
                    self.name,
                    overwrite=overwrite,
                )

        logger.info(f"Node '{self.name}': execution completed.")
        return self.table

    def _execute_multithreaded(
        self,
        tables: Dict[str, Table] = None,
        con: Optional[object] = None,
        overwrite: bool = False,
        lazy_execution: bool = False,
        n_threads: int = 4,
    ) -> Table:
        """
        Execute this node's dependencies using multithreading, then execute this node.
        """
        if tables is None:
            tables = {}

        # Build dependency graph for all dependencies
        all_deps = self.dependencies
        nodes = {node.name: node for node in all_deps}
        nodes[self.name] = self  # Add self to the nodes

        # Validate node uniqueness
        self._validate_node_uniqueness(nodes)

        # Build dependency and reverse graphs
        dependency_graph = self._build_dependency_graph(nodes)
        reverse_graph = self._build_reverse_graph(dependency_graph)

        # Execute dependencies in parallel if n_threads > 1
        if n_threads == 1 or len(all_deps) <= 1:
            return self._execute_sequential(tables, con, overwrite, lazy_execution)

        # Track completion status and results
        completed = set()
        completion_lock = threading.Lock()

        # Track in-degree for scheduling
        in_degree = {}
        for node_name, dependencies in dependency_graph.items():
            in_degree[node_name] = len(dependencies)
        for node_name in nodes:
            if node_name not in in_degree:
                in_degree[node_name] = 0

        # Queue for nodes ready to execute
        ready_queue = queue.Queue()

        # Add nodes with no dependencies to ready queue
        for node_name, degree in in_degree.items():
            if degree == 0:
                ready_queue.put(node_name)

        def worker():
            """Worker function for thread pool"""
            while True:
                try:
                    node_name = ready_queue.get(timeout=1)
                    if node_name is None:  # Sentinel value to stop worker
                        break
                except queue.Empty:
                    break

                try:
                    logger.info(
                        f"Thread {threading.current_thread().name}: executing node '{node_name}'"
                    )
                    node = nodes[node_name]

                    # Execute the node (without recursive child execution since we handle dependencies here)
                    if lazy_execution:
                        if not overwrite:
                            raise ValueError(
                                "lazy_execution only works with overwrite=True."
                            )
                        if con is None:
                            raise ValueError(
                                "A DatabaseConnector is required for lazy execution."
                            )

                        if node._get_current_hash() != node._get_last_hash():
                            logger.info(f"Node '{node_name}': computing...")
                            table = node._execute(tables)
                            if (
                                table is not None
                            ):  # Only create table if _execute returns something
                                con.create_table(table, node_name, overwrite=overwrite)
                            node._update_current_hash()
                        else:
                            logger.info(
                                f"Node '{node_name}': unchanged, using cached result"
                            )
                            table = con.get_dest_table(node_name)
                    else:
                        table = node._execute(tables)
                        if (
                            con and table is not None
                        ):  # Only create table if _execute returns something
                            con.create_table(table, node_name, overwrite=overwrite)

                    node.table = table

                    with completion_lock:
                        completed.add(node_name)

                        # Update in-degree for dependent nodes and add ready ones to queue
                        for dependent in reverse_graph.get(node_name, set()):
                            in_degree[dependent] -= 1
                            if in_degree[dependent] == 0:
                                # Check if all dependencies are completed
                                deps_completed = all(
                                    dep in completed
                                    for dep in dependency_graph.get(dependent, set())
                                )
                                if deps_completed:
                                    ready_queue.put(dependent)

                    logger.info(
                        f"Thread {threading.current_thread().name}: completed node '{node_name}'"
                    )

                except Exception as e:
                    logger.error(f"Error executing node '{node_name}': {str(e)}")
                    raise
                finally:
                    ready_queue.task_done()

        # Start worker threads
        threads = []
        for i in range(min(n_threads, len(nodes))):
            thread = threading.Thread(target=worker, name=f"PhenexWorker-{i}")
            thread.daemon = True
            thread.start()
            threads.append(thread)

        # Wait for all nodes to complete
        while len(completed) < len(nodes):
            threading.Event().wait(0.1)  # Small delay to prevent busy waiting

        # Signal workers to stop and wait for them
        for _ in threads:
            ready_queue.put(None)  # Sentinel value to stop workers

        for thread in threads:
            thread.join(timeout=1)

        logger.info(
            f"Node '{self.name}': completed multithreaded execution of {len(nodes)} nodes"
        )
        return self.table

    def _validate_node_uniqueness(self, nodes: Dict[str, "Node"]):
        """
        Validate that all nodes are unique according to the rule:
        node1.name == node2.name implies hash(node1) == hash(node2)

        This ensures that nodes with the same name have identical parameters (same hash).
        """
        name_to_hash = {}

        for node_name, node in nodes.items():
            node_hash = hash(node)

            # Check if we've seen this name before
            if node_name in name_to_hash:
                existing_hash = name_to_hash[node_name]
                if existing_hash != node_hash:
                    raise ValueError(
                        f"Node uniqueness violation: Found nodes with the same name '{node_name}' "
                        f"but different hashes ({existing_hash} vs {node_hash}). "
                        f"Nodes with the same name must have identical parameters."
                    )
            else:
                name_to_hash[node_name] = node_hash

    def _build_dependency_graph(self, nodes: Dict[str, "Node"]) -> Dict[str, Set[str]]:
        """
        Build a dependency graph where each node maps to its direct dependencies (children).
        """
        graph = defaultdict(set)
        for node_name, node in nodes.items():
            for child in node.children:
                if child.name in nodes:
                    graph[node_name].add(child.name)
        return dict(graph)

    def _build_reverse_graph(
        self, dependency_graph: Dict[str, Set[str]]
    ) -> Dict[str, Set[str]]:
        """
        Build a reverse dependency graph where each node maps to nodes that depend on it (parents).
        """
        reverse_graph = defaultdict(set)
        for node_name, dependencies in dependency_graph.items():
            for dep in dependencies:
                reverse_graph[dep].add(node_name)
        return dict(reverse_graph)

    def _execute(self, tables: Dict[str, Table] = None) -> Table:
        """
        Implements the processing logic for this node. Should be implemented by subclasses to define specific computation logic.

        Parameters:
            tables (Dict[str, Table]): A dictionary where the keys are table domains and the values are Table objects.

        Raises:
            NotImplementedError: This method should be implemented by subclasses.
        """
        raise NotImplementedError()

    def __eq__(self, other: "Node") -> bool:
        return hash(self) == hash(other)

    def diff(self, other: "Node"):
        return DeepDiff(self.to_dict(), other.to_dict(), ignore_order=True)

    def to_dict(self):
        """
        Return a dictionary representation of the Node. The dictionary must contain all dependencies of the Node such that if anything in self.to_dict() changes, the Node must be recomputed.
        """
        return to_dict(self)

    def __repr__(self):
        return f"Node('{self.name}')"

    def visualize_dependencies(self) -> str:
        """
        Create a text visualization of the dependency graph for this node and its dependencies.

        Returns:
            str: A text representation of the dependency graph
        """
        lines = [f"Dependencies for Node '{self.name}':"]

        # Get all dependencies
        all_deps = self.dependencies
        nodes = {node.name: node for node in all_deps}
        nodes[self.name] = self  # Add self to the nodes

        # Build dependency graph
        dependency_graph = self._build_dependency_graph(nodes)

        for node_name in sorted(nodes.keys()):
            dependencies = dependency_graph.get(node_name, set())
            if dependencies:
                deps_str = ", ".join(sorted(dependencies))
                lines.append(f"  {node_name} depends on: {deps_str}")
            else:
                lines.append(f"  {node_name} (no dependencies)")

        return "\n".join(lines)

dependencies property

Recursively collect all dependencies of a node (including dependencies of dependencies).

Returns:

Type	Description
Set[Node]	List[Node]: A list of Node objects on which this Node depends.

dependency_graph property

Build a dependency graph where each node maps to its direct dependencies (children).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, Set[Node]: A mapping of Node's to their children Node's.

reverse_dependency_graph property

Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, List[Node]: A mapping of Node's to their parent Node's.

execute(tables=None, con=None, overwrite=False, lazy_execution=False, n_threads=1)

Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

Parameters:

Name	Type	Description	Default
tables	Dict[str, Table]	A dictionary mapping domains to Table objects.	None
con	Optional[object]	Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.	None
overwrite	bool	If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.	False
lazy_execution	bool	If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.	False
n_threads	int	Max number of Node's to execute simultaneously when this node has multiple children.	1

Returns:

Name	Type	Description
Table	Table	The resulting table for this node. Also accessible through self.table after calling self.execute().

Source code in phenex/node.py

def execute(
    self,
    tables: Dict[str, Table] = None,
    con: Optional[object] = None,
    overwrite: bool = False,
    lazy_execution: bool = False,
    n_threads: int = 1,
) -> Table:
    """
    Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

    Parameters:
        tables: A dictionary mapping domains to Table objects.
        con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.
        overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.
        lazy_execution: If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.
        n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

    Returns:
        Table: The resulting table for this node. Also accessible through self.table after calling self.execute().
    """
    # Handle None tables
    if tables is None:
        tables = {}

    # Use multithreaded execution if we have multiple children and n_threads > 1
    if len(self.children) > 1 and n_threads > 1:
        return self._execute_multithreaded(
            tables, con, overwrite, lazy_execution, n_threads
        )
    else:
        return self._execute_sequential(tables, con, overwrite, lazy_execution)

to_dict()

Return a dictionary representation of the Node. The dictionary must contain all dependencies of the Node such that if anything in self.to_dict() changes, the Node must be recomputed.

Source code in phenex/node.py

def to_dict(self):
    """
    Return a dictionary representation of the Node. The dictionary must contain all dependencies of the Node such that if anything in self.to_dict() changes, the Node must be recomputed.
    """
    return to_dict(self)

visualize_dependencies()

Create a text visualization of the dependency graph for this node and its dependencies.

Returns:

Name	Type	Description
str	str	A text representation of the dependency graph

Source code in phenex/node.py

def visualize_dependencies(self) -> str:
    """
    Create a text visualization of the dependency graph for this node and its dependencies.

    Returns:
        str: A text representation of the dependency graph
    """
    lines = [f"Dependencies for Node '{self.name}':"]

    # Get all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency graph
    dependency_graph = self._build_dependency_graph(nodes)

    for node_name in sorted(nodes.keys()):
        dependencies = dependency_graph.get(node_name, set())
        if dependencies:
            deps_str = ", ".join(sorted(dependencies))
            lines.append(f"  {node_name} depends on: {deps_str}")
        else:
            lines.append(f"  {node_name} (no dependencies)")

    return "\n".join(lines)

NodeGroup

Bases: Node

A NodeGroup is a simple grouping mechanism for nodes that should run together. It is a no-op node that returns no table and is simply used to enforce dependencies and organize related nodes.

The NodeGroup acts as a container that ensures all its child nodes are executed when the group is executed. It does not perform any computation itself and returns None from its _execute method.

Parameters:

Name	Type	Description	Default
name	str	A short and descriptive name for the NodeGroup.	required
nodes	List[Node]	A list of Node objects to be grouped together. When the NodeGroup is executed, all these nodes (and their dependencies) will be executed.	required

Source code in phenex/node.py

class NodeGroup(Node):
    """
    A NodeGroup is a simple grouping mechanism for nodes that should run together. It is a no-op node that returns no table and is simply used to enforce dependencies and organize related nodes.

    The NodeGroup acts as a container that ensures all its child nodes are executed when the group is executed. It does not perform any computation itself and returns None from its _execute method.

    Parameters:
        name: A short and descriptive name for the NodeGroup.
        nodes: A list of Node objects to be grouped together. When the NodeGroup is executed, all these nodes (and their dependencies) will be executed.
    """

    def __init__(self, name: str, nodes: List[Node]):
        super(NodeGroup, self).__init__(name=name)
        self.add_children(nodes)

    def _execute(self, tables: Dict[str, Table] = None) -> Table:
        """
        Execute all children nodes and return a table with information about dependencies.
        The execution logic is handled by the parent Node class.
        """
        # Create a table with NODE_NAME and NODE_PARAMS for each dependency
        data = []
        for node in self.dependencies:
            data.append(
                {"NODE_NAME": node.name, "NODE_PARAMS": json.dumps(node.to_dict())}
            )

        # Create a pandas DataFrame and convert to ibis memtable
        df = pd.DataFrame(data)
        return ibis.memtable(df)

dependencies property

Recursively collect all dependencies of a node (including dependencies of dependencies).

Returns:

Type	Description
Set[Node]	List[Node]: A list of Node objects on which this Node depends.

dependency_graph property

Build a dependency graph where each node maps to its direct dependencies (children).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, Set[Node]: A mapping of Node's to their children Node's.

reverse_dependency_graph property

Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, List[Node]: A mapping of Node's to their parent Node's.

execute(tables=None, con=None, overwrite=False, lazy_execution=False, n_threads=1)

Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

Parameters:

Name	Type	Description	Default
tables	Dict[str, Table]	A dictionary mapping domains to Table objects.	None
con	Optional[object]	Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.	None
overwrite	bool	If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.	False
lazy_execution	bool	If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.	False
n_threads	int	Max number of Node's to execute simultaneously when this node has multiple children.	1

Returns:

Name	Type	Description
Table	Table	The resulting table for this node. Also accessible through self.table after calling self.execute().

Source code in phenex/node.py

def execute(
    self,
    tables: Dict[str, Table] = None,
    con: Optional[object] = None,
    overwrite: bool = False,
    lazy_execution: bool = False,
    n_threads: int = 1,
) -> Table:
    """
    Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

    Parameters:
        tables: A dictionary mapping domains to Table objects.
        con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.
        overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.
        lazy_execution: If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.
        n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

    Returns:
        Table: The resulting table for this node. Also accessible through self.table after calling self.execute().
    """
    # Handle None tables
    if tables is None:
        tables = {}

    # Use multithreaded execution if we have multiple children and n_threads > 1
    if len(self.children) > 1 and n_threads > 1:
        return self._execute_multithreaded(
            tables, con, overwrite, lazy_execution, n_threads
        )
    else:
        return self._execute_sequential(tables, con, overwrite, lazy_execution)

to_dict()

Return a dictionary representation of the Node. The dictionary must contain all dependencies of the Node such that if anything in self.to_dict() changes, the Node must be recomputed.

Source code in phenex/node.py

def to_dict(self):
    """
    Return a dictionary representation of the Node. The dictionary must contain all dependencies of the Node such that if anything in self.to_dict() changes, the Node must be recomputed.
    """
    return to_dict(self)

visualize_dependencies()

Create a text visualization of the dependency graph for this node and its dependencies.

Returns:

Name	Type	Description
str	str	A text representation of the dependency graph

Source code in phenex/node.py

def visualize_dependencies(self) -> str:
    """
    Create a text visualization of the dependency graph for this node and its dependencies.

    Returns:
        str: A text representation of the dependency graph
    """
    lines = [f"Dependencies for Node '{self.name}':"]

    # Get all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency graph
    dependency_graph = self._build_dependency_graph(nodes)

    for node_name in sorted(nodes.keys()):
        dependencies = dependency_graph.get(node_name, set())
        if dependencies:
            deps_str = ", ".join(sorted(dependencies))
            lines.append(f"  {node_name} depends on: {deps_str}")
        else:
            lines.append(f"  {node_name} (no dependencies)")

    return "\n".join(lines)